--- title: "xgboost vs alpaca-lora" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dmlc-xgboost-vs-tloen-alpaca-lora" tools: ["dmlc-xgboost", "tloen-alpaca-lora"] --- # xgboost vs alpaca-lora *GraphCanon updated Jul 11, 2026* ## Verdict Pick xgboost when xgboost is primarily C++; alpaca-lora is Jupyter Notebook; pick alpaca-lora when alpaca-lora is primarily Jupyter Notebook; xgboost is C++. [xgboost](https://xgboost.readthedocs.io/) reports 29k GitHub stars, 8.9k forks, and 472 open issues, last pushed Jul 10, 2026. [alpaca-lora](https://github.com/tloen/alpaca-lora) has 19k stars, 2.2k forks, and 366 open issues, last pushed Jul 29, 2024. Figures are from public GitHub metadata via [xgboost's repository](https://github.com/dmlc/xgboost) and [alpaca-lora's repository](https://github.com/tloen/alpaca-lora). | | [xgboost](/tools/dmlc-xgboost.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Tagline | Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow | Instruct-tune LLaMA on consumer hardware | | Stars | 28,553 | 18,913 | | Forks | 8,881 | 2,185 | | Open issues | 472 | 366 | | Language | C++ | Jupyter Notebook | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Computer Vision | Model Training, Inference & Serving, Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [xgboost](/tools/dmlc-xgboost.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 1d | 712d | | Open issues (now) | 472 | 366 | | Owner type | Organization | User | | Security scan | No lockfile | 1 critical, 5 high, 12 medium, 28 low (1 critical, 5 high, 12 medium, 28 low) | | Full report | [trust report](/tools/dmlc-xgboost/trust.md) | [trust report](/tools/tloen-alpaca-lora/trust.md) | ## Choose when ### Choose xgboost if… - xgboost is primarily C++; alpaca-lora is Jupyter Notebook. - Tags unique to xgboost: gbdt, machine-learning, gbrt, c++. - More GitHub stars (29k vs 19k) - visibility, not fit. ### Choose alpaca-lora if… - alpaca-lora is primarily Jupyter Notebook; xgboost is C++. - Tags unique to alpaca-lora: jupyter notebook. - Also covers Model Training, Inference & Serving. - alpaca-lora ships Docker support for self-hosted deployment. ## When NOT to use alpaca-lora - Last GitHub push was 712 days ago (dormant maintenance, Jul 29, 2024). Validate activity before betting a new project on alpaca-lora. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## Common questions ### What is the difference between xgboost and alpaca-lora? xgboost: Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories. ### When should I choose xgboost over alpaca-lora? Choose xgboost over alpaca-lora when xgboost is primarily C++; alpaca-lora is Jupyter Notebook; Tags unique to xgboost: gbdt, machine-learning, gbrt, c++; More GitHub stars (29k vs 19k) - visibility, not fit. ### When should I choose alpaca-lora over xgboost? Choose alpaca-lora over xgboost when alpaca-lora is primarily Jupyter Notebook; xgboost is C++; Tags unique to alpaca-lora: jupyter notebook; Also covers Model Training, Inference & Serving; alpaca-lora ships Docker support for self-hosted deployment. ### When should I avoid alpaca-lora? Last GitHub push was 712 days ago (dormant maintenance, Jul 29, 2024). Validate activity before betting a new project on alpaca-lora. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### Is xgboost or alpaca-lora more popular on GitHub? xgboost has more GitHub stars (28,553 vs 18,913). Stars measure visibility, not whether either tool fits your constraints. ### Are xgboost and alpaca-lora open source? Yes - both are open-source projects on GitHub (xgboost: Apache-2.0, alpaca-lora: Apache-2.0). ### Where can I find alternatives to xgboost or alpaca-lora? GraphCanon lists graph-backed alternatives at [xgboost alternatives](/tools/dmlc-xgboost/alternatives) and [alpaca-lora alternatives](/tools/tloen-alpaca-lora/alternatives) ([xgboost markdown twin](/tools/dmlc-xgboost/alternatives.md), [alpaca-lora markdown twin](/tools/tloen-alpaca-lora/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/dmlc-xgboost-vs-tloen-alpaca-lora.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, xgboost or alpaca-lora? xgboost: Very active. alpaca-lora: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for xgboost and alpaca-lora? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [xgboost trust report](/tools/dmlc-xgboost/trust); [alpaca-lora trust report](/tools/tloen-alpaca-lora/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dmlc-xgboost`](/api/graphcanon/graph?tool=dmlc-xgboost) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_